According to the IEEE 802.11 standard and in particular its 802.11n-2009 amendment, a physical layer OFDM frame (a ‘WLAN frame’ or a ‘frame’) begins with an STF (short training field) followed by an LTF (long training field). In frames with so-called ‘legacy’ format and frames with mixed mode format, it is L-STF and L-LTF; in frames with green field format, it is HT-GF-STF and HT-GF-LTF1.
An important role of the STF and first LTF is to allow a receiver to estimate the start time (to establish time synchronization) for a received frame. Time synchronization is a sensitive part of 802.11 OFDM receiver design; large synchronisation timing errors introduce inter-symbol interference (ISI) that severely degrades reception. However, because OFDM symbols include a guard interval, small synchronization timing errors may be tolerated without much degradation of the receiver performance.
The STF consists of 10 identical short training symbols, and it is typically used for AGC (automatic gain control), synchronization and coarse frequency offset estimation. Autocorrelation of the STF field with a 0.8 μs time shift generates a slowly varying triangle shaped autocorrelation curve. The triangle's peak provides an estimate of the frame start time tSTF. However, this autocorrelation peak is sensitive to noise; moderate noise can move the autocorrelation peak by 100-200 ns. Furthermore, autocorrelation with a 0.8 μs time shift can produce false detection in the presence of tone interference or narrow-band interference whose period is a sub-multiple of 0.8 μs.
The STF is followed by an LTF, which includes two identical long training symbols, each 3.2 μs long with a 0.8 μs guard interval. For received signals that are not subject to fading, cross-correlation of the long training symbols with the LTF symbol template generates a sharp peak. The peak provides LTF synchronization timing and reliability information; the peak position is not very sensitive to noise while the peak value generally becomes lower with increasing noise level. LTF synchronization based on cross-correlation can be used to validate detections by an STF autocorrelation synchronizer in order to discard false STF detections, but it is sensitive to the multipath effect, especially when a late arrival path is strong.
The combination of the above two synchronization methods, coarse synchronization timing estimation based on STF autocorrelation followed by verification and fine timing adjustment of LTF cross-correlation, works well when there is only one transmitter space time stream.
In order to increase system throughput, according to IEEE 802.11n-2009, multiple transmitter space time streams are transmitted simultaneously. To prevent unintentional beamforming when similar signals are transmitted in different space time streams, two sets of cyclic time shift values are applied to the non-HT portion and the HT portion of frames respectively. These cyclic time shift values are defined in the IEEE 802.11n-2009 amendment (see table 20-8 and 20-9). The time shift values can be as high as −200 ns and −600 ns for non-HT and HT portion respectively. However, the cyclic time shift introduces a pseudo multipath problem which can cause cross-correlation based time synchronisation algorithms to fail. FIG. 1 is an example of failed LTF synchronization due to the pseudo multipath problem.
The data shown in FIG. 1 is for a receiver operating in HT-GF mode, with a sample period of 50 ns. Due to the −400 ns cyclic time shift introduced in the second transmitter space time stream, there are two peaks occurring in the LTF cross-correlation profile, the late one being the correct synchronization time. Because the first peak is stronger than the second, a receiver that uses the strongest peak to estimate synchronization timing will make a 400 ns synchronization timing error in this case.
With the increase in the number of transmitter space time streams, the number of LTF cross-correlation peaks caused by the cyclic time shift increases, causing their merger or mutual cancellation, so that the LTF cross-correlation profile becomes more complicated. This is called the pseudo multipath effect.
Most synchronization methods for OFDM WLAN are either autocorrelation based algorithms or cross-correlation based algorithms. For autocorrelation based algorithms, when the SNR is not very high, the timing error will be large; autocorrelation also suffers from false detection caused by tone interference or narrow-band interference whose period is a sub-multiple of 0.8 μs. Conventional cross-correlation based algorithms suffer from the pseudo multipath effect in MIMO OFDM WLAN.
Other synchronization methods use a maximum likelihood estimation to achieve better performance; however their complexity is too high for practical implementation. See E. G. Larsson, et al, “Joint Symbol Timing and Channel Estimation for OFDM Based WLANs”, IEEE Commun. Letters, vol. 5, no. 8, pp. 325-327, August 2001.
Another class of synchronisation methods relies on the fact that the signal transmitted during the guard interval of each OFDM symbol is repeated at the end of the symbol. See T. M. Schmidl and D. C. Cox, “Robust Frequency and Timing Synchronization for OFDM”, IEEE Trans. On Communications, vol. 45, no. 12, pp. 1613-1621, December 1997. However, these methods are more suitable for synchronisation to continuous streams of OFDM symbols, such as in DAB or DVB, rather than for frame transmissions as defined in IEEE 802.11n-2009.
To tackle the pseudo multipath effect in an 802.11n OFDM WLAN system, a three-step timing synchronization method is proposed in Dong Wang, Jinyun Zhang, “Timing Synchronization for MIMO-OFDM WLAN Systems”, IEEE Wireless Communications and Networking Conference, 2007. WCNC 2007, pp 1178-1183. In the first step, a sliding window differentiator is concatenated with an auto-correlator to remove the auto-correlation plateau; in the second step, a SIR (signal-to-interference ratio) metric is calculated based on the cross-correlation output in a small search window around the estimated coarse timing position. In the third step, the frame timing is refined in a small window around the estimation from the second step. The implementation complexity is high; also the performance of the algorithm depends heavily on selection of parameter values.